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Beamforming Design for Multi-Cell SWIPT-Enabled Wireless Networks 6

Beamforming Design for Multi-Cell SWIPT-Enabled Wireless Net...

引用

6th IEEE International Conference on computer and Communication engineering technology, CCET 2023

作者： Liu, JainShing Chang, Wan-Ling Providence University Computer Science and Information Engineering Taichung Taiwan Chaoyang University of Technology Insurance Taichung Taiwan

ISBN: (纸本)9798350340686

In simultaneous wireless information and power transfer (SWIPT) wireless networks, beamforming and power split techniques are essential for optimizing the tradeoff between energy harvesting and information decoding. In the context of multi-cell multi-input single-output (MISO) networks, where the challenge of longer distance wireless charging is added to conventional information transmission, we propose a joint optimization approach for beamforming and power splitting, while satisfying both signal-to-interference-and-noise (SINR) and energy harvesting (EH) requirements. Specifically, we propose two distinct programming models for minimizing the total transmit power, and analyze their performance trends under different SINR and EH requirements. Then, a simulation study is conducted to verify the proposed models and provide insights into the design of efficient SWIPT schemes. Finally, through the simulation study, these models can be shown to have the performance benefits that can approach optimal results with fewer iterations compared to conventional optimization tools. © 2023 IEEE.

关键词： Beamforming

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Research on Amplification Algorithm of Small Sample Soil Composite Data Based on Probability Distribution 7

Research on Amplification Algorithm of Small Sample Soil Com...

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7th International Conference on computer Network, Electronic and Automation, ICCNEA 2024

作者： Zhang, Shuyue Liang, Xiangyang Song, Yang School of Computer Science and Engineering Xi’an Technological University Xi’an China Shaanxi Kaizhou Information Technology Co. Ltd Xi’an China

Analyzing and studying the data of soil compounds from 10 cities, two important features were found in this project. First, the data for all compounds in soil follow a normal distribution with different parameters. Therefore, this paper argues that small-sample soil compound amplification data should also have these two characteristics. Based on this, this paper proposes a new algorithm called PDWCC (probability distribution with correlation coefficient) for amplification of small-sample soil compound data. The algorithm combines a probability distribution with a Pearson correlation coefficient. In addition, an optimized genetic algorithm incorporating a mutation operator was introduced to improve the efficiency of data amplification. Finally, experimental comparisons were made with conventional Bootstrap and SMOTE amplification algorithms using SVM classifiers. The results show that the PDWCC amplification algorithm outperforms the other algorithms in terms of classification accuracy, recall and F1 value with mean values of 81.77%, 79.34% and 78.65%, respectively. © 2024 Institute of Physics Publishing. All rights reserved.

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Efficient Deep Molecular Dynamic Model Training on Heterogeneous System 29

Efficient Deep Molecular Dynamic Model Training on Heterogen...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Du, Shaokang You, Xin Yang, Hailong Shang, Jing Xiao, Zhiwen Wu, Zhihui Luan, Zhongzhi Qian, Depei Beihang University School of Computer Science and Engineering Beijing China China Mobile Information Technology Center China

ISBN: (纸本)9798350330717

Molecular dynamics is a widely adopted simulation method for analyzing the movement of atoms and molecules. Traditional molecular dynamics simulation methods are computationally intensive and difficult to simulate a large number of atoms. In contrast, molecular dynamics based on deep potential models such as DeePMD can leverage deep learning techniques to improve simulation efficiency. Although DeePMD has incorporated mainstream deep learning frameworks, it still suffers from low performance and efficiency during its model training on heterogeneous systems such as CPU and GPU. Particularly, a large number of operators cannot be accelerated by GPU, resulting in low utilization of GPU computational resources. In this paper, we comprehensively analyze the computational bottlenecks and the corresponding root causes of DeePMD. We correspondingly propose several novel optimization strategies. Specifically, for preprocessing, we identify the computation redundancies and the GPU parallelization opportunities for performance optimization. For training, we propose optimization strategies such as operator fusion, redundancy elimination, and concurrent execution of multiple streams and threads in the computation process. Moreover, we apply systematical optimization of computational graphs and operators. The evaluation results show that DeePMD can achieve significant speedups in several cases after applying our proposed optimizations, resulting in a maximum overall speedup of 6.36× with acceptable accuracy. © 2023 IEEE.

关键词： DeePMD GPU Performance Optimization

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Security of Examination Question Paper Through Blockchain – SecureQ

Security of Examination Question Paper Through Blockchain –...

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2024 IEEE International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Solomon Raju, G. Sowmya, K.N. Chennamma, H.R. Dept. of Computer Applications JSS Science and Technology University Mysuru India Dept. of Information Science and Engineering JSS Academy of Technical Education Bengaluru India

ISBN: (纸本)9798331505134

Ensuring the security of confidential information is a daunting task for any concerned authority. Proving the authenticity and integrity of exam question papers is a prime responsibility of examination authorities. Storing and sharing question papers on open networks makes them vulnerable to unauthorized access and alterations. The proposed approach redefines question paper management using the Interplanetary File System (IPFS) and blockchain technology, providing a secure, decentralized, and tamper-resistant platform. This solution addresses long-standing challenges and ensures security, accessibility, and data integrity, establishing new standards for examination management. A user-friendly text editor in the framework allows paper setters to create, modify, and format questions effortlessly while preserving content integrity. The integration of this editor streamlines question paper preparation and enhances both efficiency and security. By leveraging a decentralized framework, the proposed solution ensures that the critical tasks of any concerned authority are handled securely and efficiently, overcoming limitations of conventional methods. © 2024 IEEE.

关键词： Data integrity

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Enhancing Image Segmentation in the Marine Atmosphere for Time-Sensitive Contextualisation using Convolutional Long Short-Term Memory 2

Enhancing Image Segmentation in the Marine Atmosphere for Ti...

引用

2nd International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2024

作者： Reethika, A. Rajeshwari, R. Vijayalakshmi, S. Malathy, S. Gnanasaravanan, S. Suresh Kumar, M. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Information Technolog KPR Institute of Engineering and Technology Coimbatore India Department of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Information Technology Sri Ranganathar Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798331541217

The work investigates autonomous surface vehicle obstacle avoidance and detection using computer vision techniques. It recognizes boundaries in marine environments by using the temporal context from video feeds from unmanned surface vehicles. The study shows how successful LSTM cells are in the CNN topologies that are currently in use, emphasizing how they might perform better. Nevertheless, datasets and network training techniques are not perfect. The work also uses a benchmarking approach to compare new network configurations. © 2024 IEEE.

关键词： Image segmentation

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Interpretable Predictions for Crime Categories Using Log Loss Approach for Imbalanced Target Feature 2

Interpretable Predictions for Crime Categories Using Log Los...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Manasa, A. Sen, Snigdha Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383652

Crime indicates activities that infringe laws and regulations constituted by a governing authority. These wrongdoings can range from minor aberrations such as petty theft to more serious crimes like assault, robbery, or murder. Understanding crime patterns, causes, and trends is important for creating effective law enforcement strategies and enforcing policies that facilitate to a risk-free and stable society. This paper explores the interpretability of crime category predictions generated by XGBoost and Random Forest models on San Francisco crime data ranging 12 years. Notably, in comparison to other models, such as k-Nearest Neighbors (KNN), Stochastic Gradient Descent(SGD) and Naïve Bayes, our XGBoost and Random Forest models featured superior predictive excellence, Obtaining competitive log-loss scores of 2.277761 and 2.283956, respectively, to augment interpretability we have employed Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to clarify the decision-making processes of these models. This interpretability analysis would definitely add a deeper insight into the contributing factors of model's predictive behavior towards crime rate prediction. Additionally, we have dealt with imbalanced target feature as well. In comparison to existing research in the field, our work stands out by introducing advanced interpretability techniques, LIME and SHAP, enhancing transparency in crime prediction models. This innovative approach surpasses traditional methods, providing a more comprehensive understanding of crime dynamics and achieving superior predictive excellence. Innovation is introduced through the incorporation of advanced interpretability techniques, specifically Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). © 2024 IEEE.

关键词： Stochastic systems

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Image Encryption Using Chaotic Logistic and DNA Encoding 5

Image Encryption Using Chaotic Logistic and DNA Encoding

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kumar, G. Kiran Anila, M. Chandrika, C. Priyanka, M. Jayaram, D. Mohan, A. Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Gandipet Telangana Hyderabad India Chaitanya Bharathi Institute of Technology Department of Information Technology Gandipet Telangana Hyderabad India

ISBN: (纸本)9798350389609

With a rapid development in technology and digital communication, there is a need of high speed with strong secured systems. In every sector, data is communicated in various formats, among them, images are one. It is crucial to protect the image data from unauthorized access from various sources like email spoofing and spamming, malicious software, unknown access through network, cloud, storage and other related activities. Therefore, securing our image from such unauthorized access is very important. Encryption is one method of achieving high security by shielding our data from unauthorized users. It is more difficult for attackers to decipher an image when Chaotic Logistic and DNA Encoding are used for image encryption. Using this encryption technique, each image that needs to be sent is split into parts, each of which is then encrypted and sent to the recipient. Consequently, an individual possessing only one or two portions of the image will be unable to utilize it. As a result, the goal of this strategy is to achieve high security and data confidentiality. In this research, Chaotic Logistic and DNA Encoding method is used to provide security for image data that outperformed when compared to other methods. © 2024 IEEE.

关键词： Encoding (symbols)

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Importance of Blackbody in Everyday Infrared Thermography 32nd

Importance of Blackbody in Everyday Infrared Thermography

引用

32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Glavaš, Hrvoje Faculty of Electrical Engineering Computer Science and Information Technology Osijek University of Osijek Kneza Trpimira 2b Osijek31000 Croatia

ISBN: (纸本)9783031514937

Infrared thermography is a method of detecting thermal radiation, which is then associated with corresponding temperature values. Infrared thermal infrared cameras, like other measuring devices, must undergo a factory and periodic calibration process. The calibration is performed using blackbodies. This paper analyzes the practical application of the blackbody in the calibration of infrared thermal camera. Two infrared thermal cameras Flir E6 and Hikmicro M30 were calibrated with a Voltcraft IRS -350 blackbody. The calibration results are presented in tabular form, while the effect of the blackbody uncertainty on the overall measurement uncertainty is presented in graphical form. Since blackbodies are prohibitively expensive for most thermographers due to their price, calibration guidelines are provided to users when a blackbody is not available and the accuracy of the camera needs to be verified. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Calibration

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Future Directions in Recommender System: Opportunities for Improving Recommender Systems using Machine Learning and Deep Learning Techniques 2

Future Directions in Recommender System: Opportunities for I...

引用

2nd IEEE Global Conference on Communications and information Technologies, GCCIT 2024

作者： Ramachandra, H.V. Shilpa, Biradar Dr Ambedkar Institute of Technology Affiliated to Visvesvaraya Technological University Department of Computer Science and Engineering Karnataka Belagavi590 018 India Reva University School of Computer Science and Engineering Karnataka Bengaluru560064 India Dr. Ambedkar Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru560056 India

ISBN: (纸本)9798350388916

Recommender systems (RS) are algorithms which provides the users with customized suggestions for the things that are most relevant to them. The vast expansion of internet content accessibility has left users with an excessive array of options. It is therefore crucial for web platforms to offer recommendations of items to each user, in order to increase user satisfaction and engagement. This paper offers a thorough overview of the main difficulties and issues encountered in the development of recommender systems, as well as a synopsis of recent research findings and solutions. Paper began with a basic overview of recommender systems, briefly outlining the many types of RSs and covered the various research projects completed to date by numerous researchers, along with their conclusions and summaries, in the related work chapter. In the next section, we conducted an exploratory analysis of the many issues and potential fixes pertaining to recommender systems. Finally, we have summed up the various metrics needed to assess recommender system quality. © 2024 IEEE.

关键词： Collaborative learning

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Developing Automated Feature Selection Methods with Artificial Intelligence 1

Developing Automated Feature Selection Methods with Artifici...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Dehankar, Pooja Mandal, Sanjeev Kumar Hemelatha, S. Vijayaraghavan, P. Junnarkar, Aparna A. Bhambu, Pawan Ajeenkya D. Y. Patil University School of Engineering Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350359756

This studies article examines the capability of using artificial Intelligence (AI) to improve automatic feature choice strategies (AFSM). AFSM is a procedure that determines the maximum essential capabilities in a dataset, which allows to reduce fees and improve version accuracy. By using combining AI and AFSM strategies, its miles feasible to broaden greater sensible and effective characteristic selection algorithms that may discover the highest quality function combos. The research focuses in the main on supervised gaining knowledge of algorithms, together with assist Vector Machines (SVMs), to conduct the evaluation. It explains the idea of characteristic selection, provides an outline of existing AFSM strategies, and then explores capacity improvements that may come from combining AI and AFSM. It additionally offers an example of an AI-enabled AFSM set of rules and discusses viable advantages and concerns. The studies in the long run concludes that AI-enabled AFSM should provide better feature selection accuracy and better performance than traditional techniques when implemented to numerous datasets. © 2023 IEEE.

关键词： Support vector machines

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